Exploitation
M. Jahangiri; Seyed R. Ghavami Riabi; B. Tokhmechi
Abstract
Bearing in mind that lack of data is a common problem in the study of porphyry copper mining exploration, our goal was set to identify the hidden patterns within the data and to extend the information to the data-less areas. To do this, the combination of pattern recognition techniques has been used. ...
Read More
Bearing in mind that lack of data is a common problem in the study of porphyry copper mining exploration, our goal was set to identify the hidden patterns within the data and to extend the information to the data-less areas. To do this, the combination of pattern recognition techniques has been used. In this work, multi-layer neural network was used to estimate the concentration of geochemical elements. From 1755 surface and boreholes data available, analyzed by ICP, 70% was used for training, and the rest for testing. The average accuracy of estimators for 22 geochemical elements when using all data was equal to 75%. Based on validation, the optimal number of clusters for the total data was identified. The Gustafson-Kessel (GK) clustering was used to design the estimator for the geochemical element concentrations in different clusters, and the clusters were selected for estimation. The results obtained show that using GK, the estimator's average accuracy increase up to 84%. The accuracy of the elementsZn, As, Pb, Mo, and Mn with low accuracies of 0.51, 0.62, 0.64, 0.65, and 0.68 based on all data were developed to 0.76, 0.86, 0.76, 0.80, and 0.71 with the clustered data, respectively. The mean square error using all the data was 0.079, while in the case of hybrid developed method, it decreased to 0.048. There were error reductions in Al from 0.022 to 0.012, in As, from 0.105 to 0.025, and from 0.115 to 0.046 for S.
M. Abedini; M. Ziaii; Y. Negahdarzadeh; J. Ghiasi-Freez
Abstract
The porosity within a reservoir rock is a basic parameter for the reservoir characterization. The present paper introduces two intelligent models for identification of the porosity types using image analysis. For this aim, firstly, thirteen geometrical parameters of pores of each image were extracted ...
Read More
The porosity within a reservoir rock is a basic parameter for the reservoir characterization. The present paper introduces two intelligent models for identification of the porosity types using image analysis. For this aim, firstly, thirteen geometrical parameters of pores of each image were extracted using the image analysis techniques. The extracted features and their corresponding pore types of 682 pores were used for training two intelligent models, BPN (back-propagation network) and SAE (stacked autoencoder). The trained models take the geometrical properties of pores to classify the type of six porosity types including intra-particle, inter-particle, vuggy, moldic, biomoldic, and fracture. The MSE values for the BPN and SAE models were found to be 0.0042 and 0.0038, respectively. The precision, recall, and accuracy of the intelligent models for classifying the types of pores were calculated. The BPN model was able to correctly recognize 193 intra-particle pores out of 197 ones, 45 inter-particle pores out of 50 ones, 7 vuggy pores out of 9 ones, 10 moldic pores out of 12 ones, 2 biomoldic pores out of 3 ones, and 6 fractures out of 7 ones. Also the SAE model was able to correctly classify 193 intra-particle pores out of 197 ones, 46 inter-particle pores out of 50 ones, 8 vuggy pores out of 9 ones, 10 moldic pores out of 12 ones, 3 biomoldic pores out of 3 ones, and 7 fractures out of 7 ones. The results obtained showed that the SAE model carried out a bit more accuracy for classification of the inter-particle, vuggy, biomoldic, and fracture pores.
Rock Mechanics
S. Moshrefi; K. Shahriar; A. Ramezanzadeh; K. Goshtasbi
Abstract
A rock failure criterion is very important for prediction of the ultimate strength in rock mechanics and geotechnics; it is determined for rock mechanics studies in mining, civil, and oil wellborn drilling operations. Also shales are among the most difficult to treat formations. Therefore, in this research ...
Read More
A rock failure criterion is very important for prediction of the ultimate strength in rock mechanics and geotechnics; it is determined for rock mechanics studies in mining, civil, and oil wellborn drilling operations. Also shales are among the most difficult to treat formations. Therefore, in this research work, using the artificial neural network (ANN), a model was built to predict the ultimate strength of shale, and comparison was made with support vector machine (SVM), multiple linear regression models, and the widely used conventional polyaxial failure criteria in the stability analysis of rock structures, Drucker-Prager, and Mogi-Coulomb. For building the model, the corresponding results of triaxial and polyaxial tests have been performed on shales by various researchers. They were collected from reliable published articles. The results obtained showed that a feed forward back propagation multi-layer perceptron (MLP) was used and trained using the Levenberg–Marquardt algorithm, and the 2-4-1 architecture with root-mean-square-error (RMSE) of 24.41 exhibits a better performance in predicting the ultimate strength of shale in comparison with the investigated models. Also for further validation, triaxial tests were performed on the deep shale specimens. They were prepared from the Ramshire oilfield in SW Iran. The results obtained were compared with ANN, SVM, multiple linear regression models, and the conventional failure criterion prediction. They showed that the ANN model predicted ultimate strength with a minimum error and RMSE being equal to 43.81. Then the model was used for prediction of the threshold broken pressure shale layer in the Gachsaran oilfield in Iran. For this, a vertical and horizontal stress was calculated based on a depth of shale layer. The threshold broken pressure was calculated for the beginning and ending of a shale layer to be 154.21 and 167.98 Mpa, respectively.
F. Khorram; H. Memarian; B. Tokhmechi; H. Soltanian-zadeh
Abstract
In this study based on image analysis, an ore grade estimation model was developed. The study was performed at a limestone mine in central Iran. The samples were collected from different parts of the mine and crushed in size from 2.58 cm down to 15 cm. The images of the samples were taken in appropriate ...
Read More
In this study based on image analysis, an ore grade estimation model was developed. The study was performed at a limestone mine in central Iran. The samples were collected from different parts of the mine and crushed in size from 2.58 cm down to 15 cm. The images of the samples were taken in appropriate environment and processed. A total of 76 features were extracted from the identified rock samples in all images. Neural network used as an intelligent tool for ore grade estimation and the features of every image were combined with weighted average method. In order to feature dimensional decrease, principal component analysis method was used. Six principal components, which were extracted from the feature vectors, captured 90.661% of the total feature variance. Components were used as the input to neural network and four grade attributes of limestone (CaCO3, Al2O3, Fe2O3 and MgCO3) were used as the output. The root of mean squared error between the observed values and the model estimated values for the test data set are 6.378, 4.847, 0.1513 and 0.0284, the R2 values are 0.7852, 0.8663, 0.7591and 0.8094 for the mentioned chemical composition respectively. The magnitude of R2 indicates the correlation between actual and estimated data. Therefore, it can be inferred that the model can successfully estimate the limestone chemical compositions percentage.
R. Gholami; A. Moradzadeh
Abstract
Reservoir permeability is a critical parameter for characterization of the hydrocarbon reservoirs. In fact, determination of permeability is a crucial task in reserve estimation, production and development. Traditional methods for permeability prediction are well log and core data analysis which are ...
Read More
Reservoir permeability is a critical parameter for characterization of the hydrocarbon reservoirs. In fact, determination of permeability is a crucial task in reserve estimation, production and development. Traditional methods for permeability prediction are well log and core data analysis which are very expensive and time-consuming. Well log data is an alternative approach for prediction of permeability because they are usually available for all of the wells. Hence, attempts have been made to utilize well log data to predict permeability. However, because of complicate and non-linear relationship of well log and core permeability data, usual statistical and artificial methods are not completely able to provide meaningful results. In this regard, recent works on artificial intelligence have led to the introduction of a robust method generally called support vector machine (SVM). The term “SVM” is divided into two subcategories: support vector classifier (SVC) and support vector regression (SVR). The aim of this paper is to use SVR for predicting the permeability of three gas wells in South Pars filed, Iran. The results show that the overall correlation coefficient (R) between predicted and measured permeability of SVR is 0.97 compared to 0.71 of a developed general regression neural network. In addition, the strength and efficiency of SVR was proved by less time-consuming and better root mean square error in training and testing dataset.
